Name
Title | ||
|---|---|---|
Visual features based automated identification of fish species using deep convolutional neural networks. |
Abstract | ||
|---|---|---|
•Manual identification of different fish species is a difficult task.•We proposed a framework based on the CNN for fish species identification.•The proposed CNN architecture contains 32 deep layers.•We developed a data set termed as Fish-Pak from the tropical area of Pakistan.•The proposed CNN architecture is compared with state of the art CNN models. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compag.2019.105075 | Computers and Electronics in Agriculture |
Keywords | Field | DocType |
Fish species classification,VGGNet,Deeply supervised VGGNet | Computer vision,Silver carp,Pattern recognition,Convolutional neural network,Cirrhinus mrigala,Transfer of learning,Species identification,Artificial intelligence,Engineering,Deep learning,Contextual image classification | Journal |
Volume | ISSN | Citations |
167 | 0168-1699 | 2 |
PageRank | References | Authors |
0.37 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hafiz Tayyab Rauf | 1 | 14 | 6.72 |
| M. Ikram Ullah Lali | 2 | 24 | 5.65 |
| Saliha Zahoor | 3 | 2 | 0.71 |
| Syed Zakir Hussain Shah | 4 | 2 | 0.37 |
| Abd Ur Rehman | 5 | 2 | 0.71 |
| Syed Ahmad Chan Bukhari | 6 | 33 | 8.07 |